A new particle swarm optimization algorithm with adaptive genetic operator (AG-PSO) for training ANN was proposed to solve the problems appeared in the train of artificial neural network (ANN) such as the local minimum's basin of attraction and low speed. Controlled by probability, the particles were operated by genetic operator when ANN is trained by PSO algorithm. This new algorithm was used to train the ANN model of vehicle engine fault diagnosis. The result shows that the neural network trained by AG-PSO algorithm needs least amounts of iterations and achieves the better training accuracy than BP algorithm, GA and PSO algorithm.
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To improve the ability to TSP solve using GA, firstly a kind of definition of the population diversity was put forward, and then one kind of two-stage GA, setting two critical values in order to switch between greedy optimization GA and annealing partheno GA, was proposed to optimize the population in a large scale on the basis of keeping the population diversity. In the two-stage genetic algorithm, when the population diversity was degraded to some degree, it switched to the other algorithm searching the best result and improved the population diversity quickly. When the population diversity was ascended to some degree, it changed to the old algorithm, and this process repeated. The simulative result shows that the two-stage GA's convergence velocity and the searching capability are greatly improved, and that the average optimal result, the average convergence generations and the average running time are superior or the same as those of the other two GAs.
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To properly handle CGF maneuver planning in the complex battlefield environment, a method of CGF maneuver planning based on genetic algorithm was studied. First, according to the characteristic of CGF maneuver, the model of battlefield environment was established with the grid way. Second, the maneuver CGF was designed, and various behaviors of CGF were considered as keys to be studied. Third, after confirming the population coding method and the fitness function, an improved genetic algorithm for CGF maneuver path planning was proposed by optimizing various genetic operators, and then the algorithm flow was established. Finally, after establishing the process flow of CGF maneuver planning, a experimental platform based on the Netlogo was used to examine the method. Results indicate that the method is reasonable and efficient, and can properly handle CGF maneuver planning in the complex battlefield environment.
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